During the past decades, the Convolutional Neural Network (CNN) has achieved remarkable achievements in image processing [1]}, [2]}, [3]}, which also greatly promotes the development of SID.
Recently, many CNN-based models have been proposed for SID, especially for the Additive White Gaussian Noise (AWGN).
Different from traditional methods, these methods [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]} usually learn the mapping between noisy and clear images by building a well-designed CNN.
For example, Zhang et al. [4]} proposed the first end-to-end trainable CNN model (DnCNN) for Gaussian denoising, which took the advantage of batch normalization and residual learning to recover clean images.
Meanwhile, Zhang et al. [13]} also proposed a flexible FFDNet, which took the noisy image and estimated noise level map as inputs, thus a single model could deal with noise on different levels.
Liu et al. [6]} presented a multi-level wavelet CNN (WCNN) for better trade-off between the receptive field size and the computational efficiency.
Fang et al. [7]} introduced image edge prior into CNN and proposed a Multilevel Edge Features Guided Network (MLEFGN) for SID.
On the other hand, some large models have also been proposed and constantly refreshed the best results.
For instance, Zhang et al. [16]} proposed a Residual Dense Network (RDN) for image restoration with residual dense block.
Park et al. [8]} studied a Densely Connected Hierarchical Network (DHDN) by using a modified U-net architecture;
Although the above models achieve promising results, they still do not handle high-noise images well.
Meanwhile, these models are often accompanied by a large number of parameters (Figure REF ), which greatly limits their application in real scenarios.
